






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:							int 				=				argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
      )
      # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
      parser.add_argument(
          """--original_config_file""",
          default=None,
          type=str,
          help="""The YAML config file corresponding to the original architecture.""",
      )
      parser.add_argument(
          """--num_in_channels""",
          default=None,
          type=int,
          help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
      )
      parser.add_argument(
          """--scheduler_type""",
          default="""pndm""",
          type=str,
          help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
      )
      parser.add_argument(
          """--pipeline_type""",
          default=None,
          type=str,
          help=(
              """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
              """. If `None` pipeline will be automatically inferred."""
          ),
      )
      parser.add_argument(
          """--image_size""",
          default=None,
          type=int,
          help=(
              """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
              """ Base. Use 768 for Stable Diffusion v2."""
          ),
      )
      parser.add_argument(
          """--prediction_type""",
          default=None,
          type=str,
          help=(
              """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
              """ Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."""
          ),
      )
      parser.add_argument(
          """--extract_ema""",
          action="""store_true""",
          help=(
              """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
              """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
              """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
          ),
      )
      parser.add_argument(
          """--upcast_attention""",
          action="""store_true""",
          help=(
              """Whether the attention computation should always be upcasted. This is necessary when running stable"""
              """ diffusion 2.1."""
          ),
      )
      parser.add_argument(
          """--from_safetensors""",
          action="""store_true""",
          help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
      )
      parser.add_argument(
          """--to_safetensors""",
          action="""store_true""",
          help="""Whether to store pipeline in safetensors format or not.""",
      )
      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
      parser.add_argument(
          """--stable_unclip""",
          type=str,
          default=None,
          required=False,
          help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
      )
      parser.add_argument(
          """--stable_unclip_prior""",
          type=str,
          default=None,
          required=False,
          help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
      )
      parser.add_argument(
          """--clip_stats_path""",
          type=str,
          help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
          required=False,
      )
      parser.add_argument(
          """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
      )
      parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
      parser.add_argument(
          """--vae_path""",
          type=str,
          default=None,
          required=False,
          help="""Set to a path, hub id to an already converted vae to not convert it again.""",
      )
      SCREAMING_SNAKE_CASE__		:							List[str] 				=				parser.parse_args()

      SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				download_from_original_stable_diffusion_ckpt(
          checkpoint_path=args.checkpoint_path,
          original_config_file=args.original_config_file,
          image_size=args.image_size,
          prediction_type=args.prediction_type,
          model_type=args.pipeline_type,
          extract_ema=args.extract_ema,
          scheduler_type=args.scheduler_type,
          num_in_channels=args.num_in_channels,
          upcast_attention=args.upcast_attention,
          from_safetensors=args.from_safetensors,
          device=args.device,
          stable_unclip=args.stable_unclip,
          stable_unclip_prior=args.stable_unclip_prior,
          clip_stats_path=args.clip_stats_path,
          controlnet=args.controlnet,
          vae_path=args.vae_path,
      )

      if args.half:
            pipe.to(torch_dtype=torch.floataa)

      if args.controlnet:
            # only save the controlnet model
            pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
      else:
            pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)














'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(
             UpperCamelCase__   ,	split=UpperCamelCase__   ,	features=UpperCamelCase__   ,	cache_dir=UpperCamelCase__   ,	keep_in_memory=UpperCamelCase__   ,	streaming=UpperCamelCase__   ,	num_proc=UpperCamelCase__   ,	**UpperCamelCase__   ,	)
         a_						     =			field
         a_						     =			path_or_paths if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else {self.split: path_or_paths}
         a_						     =			Json(
             cache_dir=UpperCamelCase__   ,	data_files=UpperCamelCase__   ,	features=UpperCamelCase__   ,	field=UpperCamelCase__   ,	**UpperCamelCase__   ,	)







  def    _a       (			self	):






         """simple docstring"""


         if self.streaming:
                a_						     =			self.builder.as_streaming_dataset(split=self.split	)
         # Build regular (map-style) dataset
         else:
                a_						     =			None
                a_						     =			None
                a_						     =			None
                a_						     =			None

                self.builder.download_and_prepare(
                    download_config=UpperCamelCase__   ,	download_mode=UpperCamelCase__   ,	verification_mode=UpperCamelCase__   ,	base_path=UpperCamelCase__   ,	num_proc=self.num_proc   ,	)
                a_						     =			self.builder.as_dataset(
                    split=self.split   ,	verification_mode=UpperCamelCase__   ,	in_memory=self.keep_in_memory	)
         return dataset





class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         if num_proc is not None and num_proc <= 0:
                raise ValueError(f'num_proc {num_proc} must be an integer > 0.'	)

         a_						     =			dataset
         a_						     =			path_or_buf
         a_						     =			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         a_						     =			num_proc
         a_						     =			'utf-8'
         a_						     =			to_json_kwargs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.to_json_kwargs.pop('path_or_buf'   ,	UpperCamelCase__	)
         a_						     =			self.to_json_kwargs.pop('orient'   ,	'records'	)
         a_						     =			self.to_json_kwargs.pop('lines'   ,	True if orient == 'records' else False	)
         a_						     =			self.to_json_kwargs.pop('index'   ,	False if orient in ['split', 'table'] else True	)
         a_						     =			self.to_json_kwargs.pop('compression'   ,	UpperCamelCase__	)

         if compression not in [None, "infer", "gzip", "bz2", "xz"]:
                raise NotImplementedError(f'`datasets` currently does not support {compression} compression'	)

         if isinstance(self.path_or_buf   ,	(str, bytes, os.PathLike)	):
                with fsspec.open(self.path_or_buf   ,	'wb'   ,	compression=UpperCamelCase__	) as buffer:
                       a_						     =			self._write(file_obj=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         else:
                if compression:
                       raise NotImplementedError(
                           f'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                           ' was passed. Please provide a local path instead.'	)
                a_						     =			self._write(
                    file_obj=self.path_or_buf   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         return written







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_     ,	a_     ,	a_     ,	a_     ,	a_						     =			args

         a_						     =			query_table(
             table=self.dataset.data   ,	key=slice(UpperCamelCase__   ,	offset + self.batch_size	)   ,	indices=self.dataset._indices   ,	)
         a_						     =			batch.to_pandas().to_json(
             path_or_buf=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**UpperCamelCase__	)
         if not json_str.endswith('\n'	):
                json_str += "\n"
         return json_str.encode(self.encoding	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			0

         if self.num_proc is None or self.num_proc == 1:
                for offset in logging.tqdm(
                    range(0   ,	len(self.dataset	)   ,	self.batch_size	)   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                       a_						     =			self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
                       written += file_obj.write(UpperCamelCase__	)
         else:
                a_     ,	a_						     =			len(self.dataset	), self.batch_size
                with multiprocessing.Pool(self.num_proc	) as pool:
                       for json_str in logging.tqdm(
                           pool.imap(
                               self._batch_json   ,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0   ,	UpperCamelCase__   ,	UpperCamelCase__	)]   ,	)   ,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                              written += file_obj.write(UpperCamelCase__	)

         return written




